SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 261–270 of 15113 papers

TitleStatusHype
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical DialogueCode2
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System CollaborationCode2
Interleaved Reasoning for Large Language Models via Reinforcement Learning—0
Incentivizing Reasoning from Weak SupervisionCode0
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning—0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning—0
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement LearningCode0
MASKSEARCH: A Universal Pre-Training Framework to Enhance Agentic Search CapabilityCode2
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified